Objective To evaluate the feasibility of wholeGtumor texture analysis of contrastGenhanced T1 WI (T1 Ce)in differentiating cystic glioma (CG)from brain abscess (BA).Methods MRI data of 25 cases of ringGenhanced CG and 24 cases of BA proven pathologically were retrospectively studied.All the patients underwent preGsurgery MRI plain and contrastGenhanced scans.FireVoxel software was used to outline the ROI of the wholeGtumor.The signal intensity histogram and related texture parameters of the 3D ROI were obtained,including mean, median,standard deviation,inhomogeneity,skewness,kurtosis and entropy.The data were first tested for normality and the differences in wholeG tumor texture analysis parameters of T1 Ce between CGs and BAs were compared using the independentGsample t test(normal distribution)and MannGWhitney rank sum test (skewed distribution).ROC curve was used to evaluate the efficacy of the parameter in differentiating CG from BA.Results There were statistical significances in the parameters of mean,median,standard deviation,inhomogeneity and skewness between CGs and BAs(P＜0.05),and there were no any statistical significances in kurtosis and entropy between CGs and BAs(P＞0.05).In all the texture parameters,the AUC of inhomogeneity was the largest(0.988),and when the threshold was 0.314, the sensitivity and the specificity were 92.60% and 9 7.1 0%,respectively.Conclusion Some of the quantitative parameters of the wholeGtumor texture analysis of T1 Ce(mean,median,standard deviation,inhomogeneity and skewness)could provide reliable and objective evidences for imaging differential diagnosis of CG and BA preGsurgery.

Objective:To evaluate the role of histogram analysis based on amide proton transfer weighted (APTw) and apparent diffusion coefficient (ADC) imaging in predicting alpha-thalassemia/mental retardation syndrome X-linked ( ATRX) mutation in isocitrate dehydrogenase ( IDH)-mutant WHO grading 2/3 gliomas. Methods:Seventy-eight patients with IDH-mutant WHO grading 2/3 gliomas, admitted to and confirmed by surgical pathology in Department of Functional Neurosurgery, Neurosurgery Center, Zhujiang Hospital, Southern Medical University from June 2017 to October 2023, including 52 with ATRX wild and 26 with ATRX mutant-type, were selected. Preoperative 3D-APTw and ADC imaging data were collected; after post-processing, the lesions were segmented using lesion outlining method based on inclusion of peri-tumor edema and lesion outlining method based on tumor entity, respectively; after that, the histogram features (the 10 th percentile, 90 th percentile, maximum, mean, median, minimum, skewness, kurtosis, entropy, range, uniformity, and variance) were extracted from 3D-APTw and ADC imaging, respectively. Univariate Logistic regression was used to compare the differences in histogram features between patients in the ATRX mutant group and ATRX wild-type group, and multivariate Logistic regression was used to screen the independent predictors for ATRX mutation (a Logistic regression prediction model was constructed). Predictive values of independent predictors and Logistic regression prediction models in ATRX mutation were evaluated by receiver operating characteristic (ROC) curve. Results:(1) With lesion outlining method based on inclusion of peri-tumor edema, univariate analysis indicated significant difference between ATRX mutant group and ATRX wild-type group in 9 histogram features: relative 3D-APTw minimum, 3D-APTw skewness, relative ADC 90 th percentile, relative ADC mean, relative ADC median, ADC kurtosis, ADC skewness, ADC uniformity, and ADC entropy ( P<0.05). With lesion outlining method based on tumor entity, univariate analysis indicated significant difference between ATRX mutant group and ATRX wild-type group in 9 histogram features: relative 3D-APTw 90 th percentile, 3D-APTw skewness, relative ADC 90 th percentile, relative ADC mean, relative ADC median, ADC kurtosis, ADC skewness, ADC uniformity and ADC entropy ( P<0.05). (2) With lesion outlining method based on inclusion of peri-tumor edema, multivariate Logistic regression showed that 3D-APTw skewness and ADC kurtosis were the independent predictor for ATRX mutation in IDH mutant WHO grading 2/3 glioma patients ( OR=0.168, 95% CI: 0.034-0.800, P=0.025; OR=0.508, 95% CI: 0.319-0.807, P=0.004). The constructed Logistic regression prediction model was P(Y=1|X)=1/1+e -(1.827-1.785×3D-APTw skewness-0.678×ADC kurtosis). With lesion outlining method based on tumor entity, multivariate Logistic regression showed that 3D-APTw skewness and ADC kurtosis were independent predictors for ATRX mutation in IDH mutant WHO grading 2/3 glioma patients ( OR=0.164, 95% CI: 0.034-0.791, P=0.024; OR=0.496, 95% CI: 0.312-0.788, P=0.003); the constructed Logistic regression prediction model was P(Y=1|X)=1/1+e -(1.585-1.810×3D-APTw skewness-0.702×ADC kurtosis). (3) ROC curve analysis showed that, with lesion outlining method based on inclusion of peri-tumor edema, area under ROC curve (AUC) of 3D-APTw skewness and ADC kurtosis was 0.725 (95% CI: 0.608-0.842, P=0.001) and 0.794 (95% CI: 0.685-0.904), respectively ( P<0.001); AUC of Logistic regression prediction model was 0.836 (95% CI: 0.729-0.942, P<0.001), and its sensitivity and specificity were 73.10% and 90.40% when the best threshold was 0.505. ROC curve showed that, with lesion outlining method based on tumor entity, AUC of 3D-APTw skewness and ADC kurtosis was 0.705 (95% CI: 0.587-0.823, P=0.003) and 0.808 (95% CI: 0.704-0.913), respectively ( P<0.001); AUC of Logistic regression prediction model was 0.844 (95% CI: 0.739-0.949, P<0.001), and its sensitivity and specificity were 84.60% and 80.80% when the best threshold was 0.399. Conclusion:Histogram analysis based on 3D-APTw and ADC imaging can predict ATRX mutation in IDH mutant WHO grading 2/3 gliomas to a certain extent.

As one of the stimulus-response polymeric intelligent materials, shape memory polymers have been widely applied in biomedicine due to their better biocompatibility, higher controllability, stronger deformation restorability and biodegradability compared with shape memory alloys and shape memory ceramics. This review will introduce the structural principles of shape memory polymers and summarize their applications in the treatment of vascular diseases, especially in endovascular therapy. At the same time, the related technical problems and the future of shape memory polymers are prospected. With the continuous development of processing technology and materials, it can be predicted that shape memory polymers will be more widely used in the medical field.

OBJECTIVE: To develop a noninvasive method for prediction of 1p/19q codeletion in diffuse lower-grade glioma (DLGG) based on multimodal magnetic resonance imaging (MRI) radiomics. METHODS: We collected MRI data from 104 patients with pathologically confirmed DLGG between October, 2015 and September, 2022. A total of 535 radiomics features were extracted from T₂WI, T₁WI, FLAIR, CE-T₁WI and DWI, including 70 morphological features, 90 first order features, and 375 texture features. We constructed logistic regression (LR), logistic regression least absolute shrinkage and selection operator (LRlasso), support vector machine (SVM) and Linear Discriminant Analysis (LDA) radiomics models and compared their predictive performance after 10-fold cross validation. The MRI images were reviewed by two radiologists independently for predicting the 1p/19q status. Receiver operating characteristic curves were used to evaluate classification performance of the radiomics models and the radiologists. RESULTS: The 4 radiomics models (LR, LRlasso, SVM and LDA) achieved similar area under the curve (AUC) in the validation dataset (0.833, 0.819, 0.824 and 0.819, respectively; P>0.1), and their predictive performance was all superior to that of resident physicians of radiology (AUC=0.645, P=0.011, 0.022, 0.016, 0.030, respectively) and similar to that of attending physicians of radiology (AUC=0.838, P>0.05). CONCLUSION Multiparametric MRI radiomics models show good performance for noninvasive prediction of 1p/19q codeletion status in patients with in diffuse lower-grade glioma.
Humans ; Magnetic Resonance Imaging ; Chromosome Aberrations ; Area Under Curve ; Glioma/genetics* ; ROC Curve

Implantation of drug-eluting stents (DES) is one of the most effective treatment for intraluminal vascular diseases such as vascular stenosis caused by atherosclerosis. Antiproliferative drugs offered by could significantly reduce the restenosis of blood vessels, which is beneficial to interventional therapy in more advanced and complex vascular diseases. This review sumarizes the state-of-the-art of the DES based on the function of loaded drug and material of the stents. We hope this review can provide basic information of DES for clinicians and researchers to make more rational choices in practical applications. Moreover, this review also propses the prospects of drug-loaded stents.